"""Unit tests for :mod:`~tmlt.core.transformations.spark_transformations.join`."""

# SPDX-License-Identifier: Apache-2.0
# Copyright Tumult Labs 2024

import re
from typing import List, Optional, Union, cast

import pandas as pd
from parameterized import parameterized
from pyspark.sql import types as st

from tmlt.core.domains.collections import DictDomain
from tmlt.core.domains.spark_domains import (
    SparkDataFrameDomain,
    SparkFloatColumnDescriptor,
    SparkIntegerColumnDescriptor,
    SparkStringColumnDescriptor,
)
from tmlt.core.exceptions import DomainKeyError, UnsupportedDomainError
from tmlt.core.metrics import (
    AddRemoveKeys,
    DictMetric,
    HammingDistance,
    IfGroupedBy,
    RootSumOfSquared,
    SumOf,
    SymmetricDifference,
)
from tmlt.core.transformations.spark_transformations.join import (
    PrivateJoin,
    PrivateJoinOnKey,
    PublicJoin,
    TruncationStrategy,
)
from tmlt.core.utils.exact_number import ExactNumberInput
from tmlt.core.utils.testing import (
    PySparkTest,
    TestComponent,
    assert_property_immutability,
    get_all_props,
)


class TestPublicJoin(TestComponent):
    """Tests for class PublicJoin.

    Tests :class:`~tmlt.core.transformations.spark_transformations.join.PublicJoin`.
    """

    def setUp(self):
        """Test setup."""
        self.input_domain = SparkDataFrameDomain(
            {"A": SparkFloatColumnDescriptor(), "B": SparkStringColumnDescriptor()}
        )
        self.public_df = self.spark.createDataFrame(
            [("X", 10.0), ("X", 11.0)],
            schema=st.StructType(
                [
                    st.StructField("B", st.StringType(), nullable=False),
                    st.StructField("C", st.DoubleType(), nullable=False),
                ]
            ),
        )
        self.private_df = self.spark.createDataFrame(
            [(1.2, "X")],
            schema=st.StructType(
                [
                    st.StructField("A", st.DoubleType(), nullable=False),
                    st.StructField("B", st.StringType(), nullable=False),
                ]
            ),
        )

    def test_constructor_mutable_arguments(self):
        """Tests that mutable constructor arguments are copied."""
        join_cols = ["B"]
        transformation = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=self.public_df,
            join_cols=join_cols,
        )
        join_cols.append("C")
        self.assertListEqual(transformation.join_cols, ["B"])

    @parameterized.expand(get_all_props(PublicJoin))
    def test_property_immutability(self, prop_name: str):
        """Tests that given property is immutable."""
        transformation = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=self.public_df,
            join_cols=["B"],
        )
        assert_property_immutability(transformation, prop_name)

    def test_properties(self):
        """PublicJoin's properties have the expected values."""
        transformation = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=self.public_df,
            join_cols=["B"],
        )
        self.assertEqual(transformation.input_domain, self.input_domain)
        self.assertEqual(transformation.input_metric, SymmetricDifference())
        self.assertEqual(
            transformation.output_domain,
            SparkDataFrameDomain(
                {
                    "B": SparkStringColumnDescriptor(),
                    "A": SparkFloatColumnDescriptor(),
                    "C": SparkFloatColumnDescriptor(allow_nan=True, allow_inf=True),
                }
            ),
        )
        self.assertEqual(transformation.output_metric, SymmetricDifference())
        self.assertEqual(transformation.join_cols, ["B"])
        pd.testing.assert_frame_equal(
            transformation.public_df.toPandas(), self.public_df.toPandas()
        )
        self.assertEqual(transformation.stability, 2)

    @parameterized.expand(
        [
            (
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=False, allow_inf=True, allow_nan=False
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=True),
                    }
                ),
                pd.DataFrame({"B": ["X", "X", None], "C": [10.0, 11.0, 3.0]}),
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=True),
                        "C": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=False, allow_nan=True
                        ),
                    }
                ),
                ["B"],
                False,
                "inner",
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=False),
                        "A": SparkFloatColumnDescriptor(
                            allow_null=False, allow_inf=True, allow_nan=False
                        ),
                        "C": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=False, allow_nan=True
                        ),
                    }
                ),
            ),
            (
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=True
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=True),
                    }
                ),
                pd.DataFrame({"A": [1.2, 1.3], "B": ["X", "X"]}),
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=False
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=False),
                    }
                ),
                ["A"],
                True,
                "inner",
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=False
                        ),
                        "B_left": SparkStringColumnDescriptor(allow_null=True),
                        "B_right": SparkStringColumnDescriptor(allow_null=False),
                    }
                ),
            ),
            (
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=False, allow_inf=True, allow_nan=False
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=True),
                    }
                ),
                pd.DataFrame({"B": ["X", "X", None], "C": [10.0, 11.0, 3.0]}),
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=True),
                        "C": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=False, allow_nan=True
                        ),
                    }
                ),
                ["B"],
                False,
                "left",
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=True),
                        "A": SparkFloatColumnDescriptor(
                            allow_null=False, allow_inf=True, allow_nan=False
                        ),
                        "C": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=False, allow_nan=True
                        ),
                    }
                ),
            ),
            (
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=True
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=True),
                    }
                ),
                pd.DataFrame({"A": [1.2, 1.3], "B": ["X", "X"]}),
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=False
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=False),
                    }
                ),
                ["A"],
                True,
                "left",
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=True
                        ),
                        "B_left": SparkStringColumnDescriptor(allow_null=True),
                        "B_right": SparkStringColumnDescriptor(allow_null=True),
                    }
                ),
            ),
        ]
    )
    def test_output_domain_special_values(
        self,
        input_domain: SparkDataFrameDomain,
        public_df: pd.DataFrame,
        public_df_domain: SparkDataFrameDomain,
        join_cols: List[str],
        join_on_nulls: bool,
        how: str,
        expected_domain: SparkDataFrameDomain,
    ):
        """Tests special values in output domain."""
        transformation = PublicJoin(
            input_domain=input_domain,
            metric=SymmetricDifference(),
            public_df=self.spark.createDataFrame(
                public_df, schema=public_df_domain.spark_schema
            ),
            public_df_domain=public_df_domain,
            join_cols=join_cols,
            join_on_nulls=join_on_nulls,
            how=how,
        )
        self.assertEqual(transformation.output_domain, expected_domain)

    @parameterized.expand(
        [
            (SymmetricDifference(), 2),
            (IfGroupedBy("B", SumOf(SymmetricDifference())), 2),
            (IfGroupedBy("B", RootSumOfSquared(SymmetricDifference())), 2),
            (IfGroupedBy("B", SymmetricDifference()), 1),
        ]
    )
    def test_public_join_correctness(
        self, metric: Union[SymmetricDifference, IfGroupedBy], d_out: int
    ):
        """Tests that public join works correctly."""
        public_join_transformation = PublicJoin(
            input_domain=self.input_domain,
            public_df=self.public_df,
            metric=metric,
            join_cols=["B"],
        )
        self.assertTrue(
            public_join_transformation.output_metric
            == metric
            == public_join_transformation.input_metric
        )
        self.assertEqual(public_join_transformation.stability_function(1), d_out)
        self.assertTrue(public_join_transformation.stability_relation(1, d_out))
        joined_df = public_join_transformation(self.private_df)
        self.assertEqual(
            joined_df.schema,
            cast(
                SparkDataFrameDomain, public_join_transformation.output_domain
            ).spark_schema,
        )
        actual = joined_df.toPandas()
        expected = pd.DataFrame(
            [[1.2, "X", 10.0], [1.2, "X", 11.0]], columns=["A", "B", "C"]
        )
        self.assert_frame_equal_with_sort(actual, expected)

    def test_public_join_overlapping_columns(self):
        """Tests that public join works when columns not used in join overlap."""
        public_df = self.spark.createDataFrame(
            pd.DataFrame(
                [["ABC", "X", 10.0], ["DEF", "X", 11.0]], columns=["A", "B", "C"]
            )
        )
        public_join_transformation = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=public_df,
            join_cols=["B"],
        )
        expected_df = pd.DataFrame(
            [[1.2, "ABC", "X", 10.0], [1.2, "DEF", "X", 11.0]],
            columns=["A_left", "A_right", "B", "C"],
        )
        actual_df = public_join_transformation(self.private_df).toPandas()
        self.assert_frame_equal_with_sort(actual_df, expected_df)

    @parameterized.expand(
        [
            (
                ["B", "C"],
                ["B"],
                "C",
                "'C' is an overlapping column but not a join key",
                SymmetricDifference(),
            ),
            (
                ["A", "B"],
                ["B"],
                "D",
                "Input metric .* and input domain .* are not compatible",
                SymmetricDifference(),
            ),
            (["A", "B"], ["B"], "A", "must be SymmetricDifference", HammingDistance()),
        ]
    )
    def test_if_grouped_by_metric_invalid_parameters(
        self,
        private_cols: List[str],
        join_cols: List[str],
        groupby_col: str,
        error_msg: str,
        inner_metric: Union[SymmetricDifference, HammingDistance],
    ):
        """Tests that PublicJoin raises appropriate errors with invalid params."""
        with self.assertRaisesRegex(ValueError, error_msg):
            PublicJoin(
                input_domain=SparkDataFrameDomain(
                    {col: SparkStringColumnDescriptor() for col in private_cols}
                ),
                public_df=self.spark.createDataFrame(
                    pd.DataFrame(
                        {"X": ["a1", "a2"], "C": ["z1", "z2"], "B": ["1", "2"]}
                    )
                ),
                metric=IfGroupedBy(groupby_col, SumOf(inner_metric)),
                join_cols=join_cols,
            )

    def test_join_with_mismatching_public_df_and_domain(self):
        """Tests that error is raised if public_df spark schema and domain mismatch."""
        with self.assertRaisesRegex(
            ValueError, "public_df's Spark schema does not match public_df_domain"
        ):
            PublicJoin(
                input_domain=self.input_domain,
                metric=SymmetricDifference(),
                public_df=self.spark.createDataFrame(
                    [("X", 10.0), ("X", 11.0)],
                    schema=st.StructType(
                        [
                            st.StructField("B", st.StringType(), nullable=True),
                            st.StructField("C", st.DoubleType(), nullable=True),
                        ]
                    ),
                ),
                public_df_domain=SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(),
                        "C": SparkFloatColumnDescriptor(),
                    }
                ),
            )

    def test_join_with_public_df_domain(self):
        """Tests that join output domain is correctly inferred from public DF domain."""
        public_join = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=self.public_df,
            public_df_domain=SparkDataFrameDomain(
                {"B": SparkStringColumnDescriptor(), "C": SparkFloatColumnDescriptor()}
            ),
        )
        actual = public_join.output_domain
        expected = SparkDataFrameDomain(
            {
                "B": SparkStringColumnDescriptor(),
                "A": SparkFloatColumnDescriptor(),
                "C": SparkFloatColumnDescriptor(),
            }
        )
        self.assertEqual(actual, expected)

    def test_join_drops_invalid_rows_from_public_df(self):
        """ "Tests that nans/infs are dropped from public DataFrame when disallowed."""
        public_join = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=self.spark.createDataFrame(
                [("X", float("nan")), ("X", float("inf")), ("X", 1.1)],
                schema=st.StructType(
                    [
                        st.StructField("B", st.StringType(), nullable=False),
                        st.StructField("C", st.DoubleType(), nullable=False),
                    ]
                ),
            ),
            public_df_domain=SparkDataFrameDomain(
                {"B": SparkStringColumnDescriptor(), "C": SparkFloatColumnDescriptor()}
            ),
        )
        actual = public_join.public_df.toPandas()
        expected = pd.DataFrame({"B": ["X"], "C": [1.1]})
        self.assert_frame_equal_with_sort(actual, expected)

    @parameterized.expand(
        [
            (
                True,
                pd.DataFrame(
                    [["X", 1.2, 1.1], [None, 0.1, 1.2], [None, 0.1, 2.1]],
                    columns=["B", "A", "C"],
                ),
            ),
            (False, pd.DataFrame([["X", 1.2, 1.1]], columns=["B", "A", "C"])),
        ]
    )
    def test_join_null_behavior(self, join_on_nulls: bool, expected: pd.DataFrame):
        """Tests that PublicJoin deals with null values on join columns correctly."""
        public_join = PublicJoin(
            input_domain=SparkDataFrameDomain(
                {
                    "A": SparkFloatColumnDescriptor(),
                    "B": SparkStringColumnDescriptor(allow_null=True),
                }
            ),
            metric=SymmetricDifference(),
            public_df=self.spark.createDataFrame(
                [(None, 2.1), (None, 1.2), ("X", 1.1)], schema=["B", "C"]
            ),
            public_df_domain=SparkDataFrameDomain(
                {
                    "B": SparkStringColumnDescriptor(allow_null=True),
                    "C": SparkFloatColumnDescriptor(allow_null=True),
                }
            ),
            join_on_nulls=join_on_nulls,
        )
        private_df = self.spark.createDataFrame(
            [(1.2, "X"), (0.1, None)], schema=["A", "B"]
        )
        actual = public_join(private_df).toPandas()
        self.assert_frame_equal_with_sort(actual, expected)

    def test_join_on_nulls_stability(self):
        """Tests that PublicJoin computes stability correctly when joining on nulls."""
        public_join = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=self.spark.createDataFrame(
                [(None, 2.1), (None, 1.2), ("X", 1.1)],
                schema=st.StructType(
                    [
                        st.StructField("B", st.StringType()),
                        st.StructField("C", st.DoubleType(), nullable=False),
                    ]
                ),
            ),
            public_df_domain=SparkDataFrameDomain(
                {
                    "B": SparkStringColumnDescriptor(allow_null=True),
                    "C": SparkFloatColumnDescriptor(),
                }
            ),
            join_on_nulls=True,
        )
        self.assertTrue(public_join.stability == 2)

    def test_join_stability_ignores_nulls(self):
        """Tests that stability is correct when join_on_nulls is False."""
        public_join = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=self.spark.createDataFrame(
                [(None, 2.1), (None, 1.2), ("X", 1.1)],
                schema=st.StructType(
                    [
                        st.StructField("B", st.StringType()),
                        st.StructField("C", st.DoubleType(), nullable=False),
                    ]
                ),
            ),
            public_df_domain=SparkDataFrameDomain(
                {
                    "B": SparkStringColumnDescriptor(allow_null=True),
                    "C": SparkFloatColumnDescriptor(),
                }
            ),
            join_on_nulls=False,
        )
        self.assertTrue(public_join.stability == 1)

    def test_empty_public_dataframe(self):
        """Tests that PublicJoin works with empty public DataFrame."""
        public_join_transformation = PublicJoin(
            input_domain=self.input_domain,
            metric=SymmetricDifference(),
            public_df=self.spark.createDataFrame([], schema=self.public_df.schema),
            join_cols=["B"],
        )
        actual = public_join_transformation(self.private_df).toPandas()
        expected = pd.DataFrame({"B": [], "A": [], "C": []})
        self.assert_frame_equal_with_sort(actual, expected)

    def test_left_join(self):
        """Tests that PublicJoin works with left join."""
        # (The left table)
        private_df = self.spark.createDataFrame(
            [(1.2, "X"), (0.1, "Y")], schema=["A", "B"]
        )
        # (The right table)
        public_df = self.spark.createDataFrame(
            [("X", 1.1), ("Z", 1.2)], schema=["B", "C"]
        )
        public_join = PublicJoin(
            input_domain=SparkDataFrameDomain(
                {
                    "A": SparkFloatColumnDescriptor(),
                    "B": SparkStringColumnDescriptor(),
                }
            ),
            metric=SymmetricDifference(),
            public_df=public_df,
            join_cols=["B"],
            how="left",
        )
        actual = public_join(private_df).toPandas()
        expected = pd.DataFrame(
            [[1.2, "X", 1.1], [0.1, "Y", None]], columns=["A", "B", "C"]
        )
        self.assert_frame_equal_with_sort(actual, expected)

    def test_invalid_how(self):
        """Tests that PublicJoin raises error for invalid how."""
        with self.assertRaisesRegex(
            ValueError, "Only 'inner' and 'left' joins are supported."
        ):
            PublicJoin(
                input_domain=self.input_domain,
                metric=SymmetricDifference(),
                public_df=self.public_df,
                join_cols=["B"],
                how="invalid",
            )


class TestPrivateJoin(PySparkTest):
    """Tests for class PrivateJoin.

    Tests :class:`~tmlt.core.transformations.spark_transformations.join.PrivateJoin`.
    """

    def setUp(self):
        """Setup."""
        self.left_domain = SparkDataFrameDomain(
            {"A": SparkIntegerColumnDescriptor(), "B": SparkStringColumnDescriptor()}
        )
        self.right_domain = SparkDataFrameDomain(
            {"B": SparkStringColumnDescriptor(), "C": SparkStringColumnDescriptor()}
        )

    def test_constructor_mutable_arguments(self):
        """Tests that mutable constructor arguments are copied."""
        join_cols = ["B"]
        transformation = PrivateJoin(
            input_domain=DictDomain(
                {"l": self.left_domain, ("r", "i", "g", "h", "t"): self.right_domain}
            ),
            left_key="l",
            right_key=("r", "i", "g", "h", "t"),
            left_truncation_strategy=TruncationStrategy.TRUNCATE,
            right_truncation_strategy=TruncationStrategy.TRUNCATE,
            left_truncation_threshold=1,
            right_truncation_threshold=1,
            join_cols=join_cols,
        )
        join_cols.append("C")
        self.assertListEqual(transformation.join_cols, ["B"])

    @parameterized.expand(get_all_props(PrivateJoin))
    def test_property_immutability(self, prop_name: str):
        """Tests that given property is immutable."""
        transformation = PrivateJoin(
            input_domain=DictDomain(
                {"l": self.left_domain, ("r", "i", "g", "h", "t"): self.right_domain}
            ),
            left_key="l",
            right_key=("r", "i", "g", "h", "t"),
            left_truncation_strategy=TruncationStrategy.TRUNCATE,
            right_truncation_strategy=TruncationStrategy.TRUNCATE,
            left_truncation_threshold=1,
            right_truncation_threshold=1,
            join_cols=["B"],
        )
        assert_property_immutability(transformation, prop_name)

    @parameterized.expand([(["B"], True), (None, False)])
    def test_properties(self, join_cols: Optional[List[str]], join_on_nulls: bool):
        """Tests that PrivateJoin's properties have expected values."""
        input_domain = DictDomain(
            {"l": self.left_domain, ("r", "i", "g", "h", "t"): self.right_domain}
        )
        transformation = PrivateJoin(
            input_domain=input_domain,
            left_key="l",
            right_key=("r", "i", "g", "h", "t"),
            left_truncation_strategy=TruncationStrategy.TRUNCATE,
            right_truncation_strategy=TruncationStrategy.TRUNCATE,
            left_truncation_threshold=1,
            right_truncation_threshold=2,
            join_cols=join_cols,
            join_on_nulls=join_on_nulls,
        )

        expected_output_metric = DictMetric(
            {
                "l": SymmetricDifference(),
                ("r", "i", "g", "h", "t"): SymmetricDifference(),
            }
        )
        expected_output_domain = SparkDataFrameDomain(
            {
                "B": SparkStringColumnDescriptor(),
                "A": SparkIntegerColumnDescriptor(),
                "C": SparkStringColumnDescriptor(),
            }
        )

        self.assertEqual(transformation.input_domain, input_domain)
        self.assertEqual(transformation.input_metric, expected_output_metric)
        self.assertEqual(transformation.output_domain, expected_output_domain)
        self.assertEqual(transformation.output_metric, SymmetricDifference())
        self.assertEqual(transformation.left_key, "l")
        self.assertEqual(transformation.right_key, ("r", "i", "g", "h", "t"))
        self.assertEqual(
            transformation.left_truncation_strategy, TruncationStrategy.TRUNCATE
        )
        self.assertEqual(
            transformation.right_truncation_strategy, TruncationStrategy.TRUNCATE
        )
        self.assertEqual(transformation.left_truncation_threshold, 1)
        self.assertEqual(transformation.right_truncation_threshold, 2)
        self.assertEqual(transformation.join_cols, ["B"])
        self.assertEqual(transformation.join_on_nulls, join_on_nulls)

    @parameterized.expand(
        [
            (left_cols, right_cols, join_cols, expected_ordering, join_on_nulls)
            for (left_cols, right_cols, join_cols, expected_ordering) in [
                (["A", "B", "C"], ["B", "D"], ["B"], ["B", "A", "C", "D"]),
                (
                    ["A", "B", "C"],
                    ["B", "D", "C"],
                    ["B"],
                    ["B", "A", "C_left", "D", "C_right"],
                ),
                (
                    ["A", "B", "C"],
                    ["B", "D", "C"],
                    ["B"],
                    ["B", "A", "C_left", "D", "C_right"],
                ),
                (["A", "B", "C"], ["B", "C", "D"], ["C", "B"], ["C", "B", "A", "D"]),
                (["A", "B"], ["B", "C"], ["B"], ["B", "A", "C"]),
            ]
            for join_on_nulls in [True, False]
        ]
    )
    def test_columns_ordering(
        self,
        left_cols: List[str],
        right_cols: List[str],
        join_cols: List[str],
        expected_ordering: List[str],
        join_on_nulls: bool,
    ):
        """Tests that the output columns of join are in expected order.

        This checks:
            - Join columns (in the order given by the user) appear first.
            - Columns of left table (with _left appended as required) appear
             next in the input order. (excluding join columns)
            - Columns of the right table (with _right appended as required) appear
             last in the input order. (excluding join columns)
        """
        left_domain = SparkDataFrameDomain(
            {col: SparkStringColumnDescriptor() for col in left_cols}
        )
        right_domain = SparkDataFrameDomain(
            {col: SparkStringColumnDescriptor() for col in right_cols}
        )

        left_df = self.spark.createDataFrame(
            [("x",) * len(left_cols)], schema=left_cols
        )
        right_df = self.spark.createDataFrame(
            [("x",) * len(right_cols)], schema=right_cols
        )

        private_join = PrivateJoin(
            input_domain=DictDomain({"left": left_domain, "right": right_domain}),
            left_key="left",
            right_key="right",
            left_truncation_strategy=TruncationStrategy.TRUNCATE,
            right_truncation_strategy=TruncationStrategy.TRUNCATE,
            left_truncation_threshold=1,
            right_truncation_threshold=1,
            join_cols=join_cols,
            join_on_nulls=join_on_nulls,
        )

        answer = private_join({"left": left_df, "right": right_df})
        self.assertTrue(answer in private_join.output_domain)
        self.assertEqual(answer.columns, expected_ordering)

    @parameterized.expand(
        [
            (1, 10, 22, TruncationStrategy.TRUNCATE),
            (5, 5, 20, TruncationStrategy.TRUNCATE),
            (1, 10, 20, TruncationStrategy.DROP),
            (5, 5, 50, TruncationStrategy.DROP),
            (
                float("inf"),
                float("inf"),
                float("inf"),
                TruncationStrategy.NO_TRUNCATION,
            ),
        ]
    )
    def test_stability_relation(
        self,
        threshold_left: Union[float, int],
        threshold_right: Union[float, int],
        d_out: Union[int, float],
        truncation_strategy: TruncationStrategy,
    ):
        """Tests that PrivateJoin's stability relation is correct."""
        join_transformation = PrivateJoin(
            input_domain=DictDomain(
                {"left": self.left_domain, ("right",): self.right_domain}
            ),
            left_key="left",
            right_key=("right",),
            left_truncation_strategy=truncation_strategy,
            right_truncation_strategy=truncation_strategy,
            left_truncation_threshold=threshold_left,
            right_truncation_threshold=threshold_right,
            join_cols=["B"],
        )
        self.assertEqual(
            join_transformation.stability_function({"left": 1, ("right",): 1}), d_out
        )

    @parameterized.expand(
        [
            (
                pd.DataFrame([(1, 2), (1, 3), (2, 4)], columns=["A", "B"]),
                pd.DataFrame([(2, 5), (1, 6)], columns=["A", "B"]),
                TruncationStrategy.TRUNCATE,
                2,
                ["A"],
                pd.DataFrame(
                    [(1, 2, 6), (1, 3, 6), (2, 4, 5)],
                    columns=["A", "B_left", "B_right"],
                ),
            ),
            (
                pd.DataFrame([(1, 2), (1, 3), (2, 4)], columns=["A", "B"]),
                pd.DataFrame([(2, 5), (1, 6)], columns=["A", "B"]),
                TruncationStrategy.DROP,
                1,
                ["A"],
                pd.DataFrame([(2, 4, 5)], columns=["A", "B_left", "B_right"]),
            ),
            (
                pd.DataFrame([(1, 2), (1, 3), (2, 4)], columns=["A", "B"]),
                pd.DataFrame([(2, 5), (2, 2), (1, 6)], columns=["A", "B"]),
                TruncationStrategy.DROP,
                1,
                ["A"],
                pd.DataFrame([], columns=["A", "B_left", "B_right"]),
            ),
            (
                pd.DataFrame([(1, 2), (1, 3), (2, 4)], columns=["A", "B"]),
                pd.DataFrame([(2, 5), (2, 2), (1, 6)], columns=["A", "B"]),
                TruncationStrategy.NO_TRUNCATION,
                float("inf"),
                ["A"],
                pd.DataFrame(
                    [(1, 2, 6), (1, 3, 6), (2, 4, 5), (2, 4, 2)],
                    columns=["A", "B_left", "B_right"],
                ),
            ),
        ]
    )
    def test_correctness(
        self,
        left: pd.DataFrame,
        right: pd.DataFrame,
        truncation_strategy: TruncationStrategy,
        threshold: Union[float, int],
        join_cols: List[str],
        expected: pd.DataFrame,
    ):
        """Tests that join is computed correctly."""
        left_domain = SparkDataFrameDomain(
            {col: SparkIntegerColumnDescriptor() for col in left.columns}
        )
        right_domain = SparkDataFrameDomain(
            {col: SparkIntegerColumnDescriptor() for col in right.columns}
        )
        private_join = PrivateJoin(
            input_domain=DictDomain({"left": left_domain, "right": right_domain}),
            left_key="left",
            right_key="right",
            left_truncation_strategy=truncation_strategy,
            right_truncation_strategy=truncation_strategy,
            left_truncation_threshold=threshold,
            right_truncation_threshold=threshold,
            join_cols=join_cols,
        )
        left_sdf = self.spark.createDataFrame(left)
        right_sdf = self.spark.createDataFrame(right)
        actual = private_join({"left": left_sdf, "right": right_sdf}).toPandas()
        self.assert_frame_equal_with_sort(actual, expected)

    @parameterized.expand(
        [
            (  # Domain contains > 2 keys
                DictDomain(
                    {
                        "df1": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                        "df2": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                        "df3": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                    }
                ),
                "df1",
                "df2",
                TruncationStrategy.TRUNCATE,
                ["A"],
                "must be a DictDomain with 2 keys",
            ),
            (  # Invalid key
                DictDomain(
                    {
                        "df1": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                        "df2": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                    }
                ),
                "df3",
                "df1",
                TruncationStrategy.TRUNCATE,
                ["A"],
                "Key 'df3' not in input domain",
            ),
            (  # Identical left and right
                DictDomain(
                    {
                        "df1": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                        "df2": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                    }
                ),
                "df1",
                "df1",
                TruncationStrategy.TRUNCATE,
                ["A"],
                "Left and right keys must be distinct",
            ),
            (  # No common columns
                DictDomain(
                    {
                        "df1": SparkDataFrameDomain(
                            {"B": SparkStringColumnDescriptor()}
                        ),
                        "df2": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                    }
                ),
                "df1",
                "df2",
                TruncationStrategy.TRUNCATE,
                None,
                "Join must involve at least one column.",
            ),
            (  # Mismatching column types
                DictDomain(
                    {
                        "df1": SparkDataFrameDomain(
                            {"A": SparkStringColumnDescriptor()}
                        ),
                        "df2": SparkDataFrameDomain(
                            {"A": SparkIntegerColumnDescriptor()}
                        ),
                    }
                ),
                "df1",
                "df2",
                TruncationStrategy.TRUNCATE,
                ["A"],
                (
                    "'A' has different data types in left (StringType) and right "
                    "(LongType) domains."
                ),
            ),
            (  # _right column already exists
                DictDomain(
                    {
                        "df1": SparkDataFrameDomain(
                            {
                                "A": SparkStringColumnDescriptor(),
                                "B": SparkStringColumnDescriptor(),
                                "B_right": SparkStringColumnDescriptor(),
                            }
                        ),
                        "df2": SparkDataFrameDomain(
                            {
                                "A": SparkStringColumnDescriptor(),
                                "B": SparkStringColumnDescriptor(),
                            }
                        ),
                    }
                ),
                "df1",
                "df2",
                TruncationStrategy.TRUNCATE,
                ["A"],
                "Name collision, 'B_right' would appear more than once in the output.",
            ),
            (  # Invalid threshold for NO_TRUNCATION strategy
                DictDomain(
                    {
                        "df1": SparkDataFrameDomain(
                            {
                                "A": SparkStringColumnDescriptor(),
                                "B": SparkStringColumnDescriptor(),
                            }
                        ),
                        "df2": SparkDataFrameDomain(
                            {
                                "A": SparkStringColumnDescriptor(),
                                "B": SparkStringColumnDescriptor(),
                            }
                        ),
                    }
                ),
                "df1",
                "df2",
                TruncationStrategy.NO_TRUNCATION,
                None,
                "The left/right_truncation_threshold must be infinite if the "
                "left/right_truncation_strategy is NO_TRUNCATION.",
            ),
        ]
    )
    def test_invalid_arguments_rejected(
        self,
        input_domain: DictDomain,
        left: str,
        right: str,
        truncation_strategy: TruncationStrategy,
        join_cols: Optional[List[str]],
        error_msg: str,
    ):
        """Tests that PrivateJoin cannot be constructed with invalid arguments."""
        with self.assertRaisesRegex(
            (ValueError, DomainKeyError, UnsupportedDomainError), re.escape(error_msg)
        ):
            PrivateJoin(
                input_domain=input_domain,
                left_key=left,
                right_key=right,
                left_truncation_strategy=truncation_strategy,
                right_truncation_strategy=truncation_strategy,
                left_truncation_threshold=1,
                right_truncation_threshold=1,
                join_cols=join_cols,
            )

    def test_join_without_nulls_changes_domain(self):
        """Test that when join_on_null=False, output domain does not allow null."""
        left_domain = SparkDataFrameDomain(
            {
                "A": SparkFloatColumnDescriptor(),
                "B": SparkStringColumnDescriptor(allow_null=True),
            }
        )
        right_domain = SparkDataFrameDomain(
            {
                "B": SparkStringColumnDescriptor(allow_null=True),
                "C": SparkFloatColumnDescriptor(allow_null=True),
            }
        )
        private_join = PrivateJoin(
            input_domain=DictDomain({"left": left_domain, "right": right_domain}),
            left_key="left",
            right_key="right",
            left_truncation_strategy=TruncationStrategy.TRUNCATE,
            right_truncation_strategy=TruncationStrategy.TRUNCATE,
            left_truncation_threshold=10,
            right_truncation_threshold=10,
            join_on_nulls=False,
        )
        expected_output_domain = SparkDataFrameDomain(
            {
                "A": SparkFloatColumnDescriptor(),
                "B": SparkStringColumnDescriptor(allow_null=False),
                "C": SparkFloatColumnDescriptor(allow_null=True),
            }
        )
        actual = private_join.output_domain
        self.assertIsInstance(actual, SparkDataFrameDomain)
        assert isinstance(actual, SparkDataFrameDomain)
        self.assertEqual(expected_output_domain["A"], actual["A"])
        self.assertEqual(expected_output_domain["B"], actual["B"])
        self.assertEqual(expected_output_domain["C"], actual["C"])

    @parameterized.expand(
        [
            (
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=False, allow_inf=True, allow_nan=False
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=False),
                    }
                ),
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=True),
                        "C": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=False, allow_nan=True
                        ),
                    }
                ),
                ["B"],
                True,
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=False),
                        "A": SparkFloatColumnDescriptor(
                            allow_null=False, allow_inf=True, allow_nan=False
                        ),
                        "C": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=False, allow_nan=True
                        ),
                    }
                ),
            ),
            (
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=True
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=True),
                    }
                ),
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=False
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=False),
                    }
                ),
                ["A"],
                True,
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=False
                        ),
                        "B_left": SparkStringColumnDescriptor(allow_null=True),
                        "B_right": SparkStringColumnDescriptor(allow_null=False),
                    }
                ),
            ),
        ]
    )
    def test_output_domain_special_values(
        self,
        left_domain: SparkDataFrameDomain,
        right_domain: SparkDataFrameDomain,
        join_cols: List[str],
        join_on_nulls: bool,
        expected_domain: SparkDataFrameDomain,
    ):
        """Tests special values in output domain."""
        transformation = PrivateJoin(
            input_domain=DictDomain({"left": left_domain, "right": right_domain}),
            left_key="left",
            right_key="right",
            left_truncation_strategy=TruncationStrategy.TRUNCATE,
            right_truncation_strategy=TruncationStrategy.TRUNCATE,
            left_truncation_threshold=10,
            right_truncation_threshold=10,
            join_cols=join_cols,
            join_on_nulls=join_on_nulls,
        )
        self.assertEqual(transformation.output_domain, expected_domain)

    @parameterized.expand(
        [
            (
                True,
                pd.DataFrame(
                    [["X", 1.2, 1.1], [None, 0.1, 1.2], [None, 0.1, 2.1]],
                    columns=["B", "A", "C"],
                ),
            ),
            (False, pd.DataFrame([["X", 1.2, 1.1]], columns=["B", "A", "C"])),
        ]
    )
    def test_join_on_nulls_behavior(self, join_on_nulls: bool, expected: pd.DataFrame):
        """Test that PrivateJoin deals with null values on join columns correctly."""
        left = self.spark.createDataFrame([(1.2, "X"), (0.1, None)], schema=["A", "B"])
        right = self.spark.createDataFrame(
            [(None, 2.1), (None, 1.2), ("X", 1.1)], schema=["B", "C"]
        )
        left_domain = SparkDataFrameDomain(
            {
                "A": SparkFloatColumnDescriptor(),
                "B": SparkStringColumnDescriptor(allow_null=True),
            }
        )
        right_domain = SparkDataFrameDomain(
            {
                "B": SparkStringColumnDescriptor(allow_null=True),
                "C": SparkFloatColumnDescriptor(allow_null=True),
            }
        )
        private_join = PrivateJoin(
            input_domain=DictDomain({"left": left_domain, "right": right_domain}),
            left_key="left",
            right_key="right",
            left_truncation_strategy=TruncationStrategy.DROP,
            right_truncation_strategy=TruncationStrategy.DROP,
            left_truncation_threshold=10,
            right_truncation_threshold=10,
            join_on_nulls=join_on_nulls,
        )
        actual = private_join({"left": left, "right": right}).toPandas()
        self.assert_frame_equal_with_sort(actual, expected)


class TestPrivateJoinOnKey(PySparkTest):
    """Tests for class PrivateJoinOnKey.

    Tests :class:`~tmlt.core.transformations.spark_transformations.join.PrivateJoinOnKey`.
    """  # pylint: disable=line-too-long

    def setUp(self):
        """Setup."""
        self.left_domain = SparkDataFrameDomain(
            {"A": SparkIntegerColumnDescriptor(), "B": SparkStringColumnDescriptor()}
        )
        self.right_domain = SparkDataFrameDomain(
            {"B": SparkStringColumnDescriptor(), "C": SparkStringColumnDescriptor()}
        )

    def test_constructor_mutable_arguments(self):
        """Tests that mutable constructor arguments are copied."""
        join_cols = ["B"]
        transformation = PrivateJoinOnKey(
            input_domain=DictDomain(
                {"l": self.left_domain, ("r", "i", "g", "h", "t"): self.right_domain}
            ),
            input_metric=AddRemoveKeys({"l": "B", ("r", "i", "g", "h", "t"): "B"}),
            left_key="l",
            right_key=("r", "i", "g", "h", "t"),
            new_key="joined",
            join_cols=join_cols,
        )
        join_cols.append("C")
        self.assertListEqual(transformation.join_cols, ["B"])

    @parameterized.expand(get_all_props(PrivateJoinOnKey))
    def test_property_immutability(self, prop_name: str):
        """Tests that given property is immutable."""
        transformation = PrivateJoinOnKey(
            input_domain=DictDomain(
                {"l": self.left_domain, ("r", "i", "g", "h", "t"): self.right_domain}
            ),
            input_metric=AddRemoveKeys({"l": "B", ("r", "i", "g", "h", "t"): "B"}),
            left_key="l",
            right_key=("r", "i", "g", "h", "t"),
            new_key="joined",
            join_cols=["B"],
        )
        assert_property_immutability(transformation, prop_name)

    @parameterized.expand([(["B"], True), (None, False)])
    def test_properties(self, join_cols: Optional[List[str]], join_on_nulls: bool):
        """Tests that PrivateJoinOnKey's properties have expected values."""
        input_domain = DictDomain(
            {"l": self.left_domain, ("r", "i", "g", "h", "t"): self.right_domain}
        )
        transformation = PrivateJoinOnKey(
            input_domain=DictDomain(
                {"l": self.left_domain, ("r", "i", "g", "h", "t"): self.right_domain}
            ),
            input_metric=AddRemoveKeys({"l": "B", ("r", "i", "g", "h", "t"): "B"}),
            left_key="l",
            right_key=("r", "i", "g", "h", "t"),
            new_key="joined",
            join_cols=join_cols,
            join_on_nulls=join_on_nulls,
        )

        expected_output_metric = AddRemoveKeys(
            {"l": "B", ("r", "i", "g", "h", "t"): "B"}
        )
        expected_output_domain = DictDomain(
            {
                "l": self.left_domain,
                ("r", "i", "g", "h", "t"): self.right_domain,
                "joined": SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(),
                        "A": SparkIntegerColumnDescriptor(),
                        "C": SparkStringColumnDescriptor(),
                    }
                ),
            }
        )

        self.assertEqual(transformation.input_domain, input_domain)
        self.assertEqual(transformation.input_metric, expected_output_metric)
        self.assertEqual(transformation.output_domain, expected_output_domain)
        self.assertEqual(
            transformation.output_metric,
            AddRemoveKeys({"l": "B", ("r", "i", "g", "h", "t"): "B", "joined": "B"}),
        )
        self.assertEqual(transformation.left_key, "l")
        self.assertEqual(transformation.right_key, ("r", "i", "g", "h", "t"))
        self.assertEqual(transformation.new_key, "joined")
        self.assertEqual(transformation.join_cols, ["B"])
        self.assertEqual(transformation.join_on_nulls, join_on_nulls)

    @parameterized.expand(
        [
            (left_cols, right_cols, join_cols, expected_ordering, join_on_nulls)
            for (left_cols, right_cols, join_cols, expected_ordering) in [
                (["A", "B", "C"], ["B", "D"], ["B"], ["B", "A", "C", "D"]),
                (
                    ["A", "B", "C"],
                    ["B", "D", "C"],
                    ["B"],
                    ["B", "A", "C_left", "D", "C_right"],
                ),
                (
                    ["A", "B", "C"],
                    ["B", "D", "C"],
                    ["B"],
                    ["B", "A", "C_left", "D", "C_right"],
                ),
                (["A", "B", "C"], ["B", "C", "D"], ["C", "B"], ["C", "B", "A", "D"]),
                (["A", "B"], ["B", "C"], ["B"], ["B", "A", "C"]),
            ]
            for join_on_nulls in [True, False]
        ]
    )
    def test_columns_ordering(
        self,
        left_cols: List[str],
        right_cols: List[str],
        join_cols: List[str],
        expected_ordering: List[str],
        join_on_nulls: bool,
    ):
        """Tests that the output columns of join are in expected order.

        This checks:
            - Join columns (in the order given by the user) appear first.
            - Columns of left table (with _left appended as required) appear
             next in the input order. (excluding join columns)
            - Columns of the right table (with _right appended as required) appear
             last in the input order. (excluding join columns)
        """
        left_domain = SparkDataFrameDomain(
            {col: SparkStringColumnDescriptor() for col in left_cols}
        )
        right_domain = SparkDataFrameDomain(
            {col: SparkStringColumnDescriptor() for col in right_cols}
        )

        left_df = self.spark.createDataFrame(
            [("x",) * len(left_cols)], schema=left_cols
        )
        right_df = self.spark.createDataFrame(
            [("x",) * len(right_cols)], schema=right_cols
        )

        private_join = PrivateJoinOnKey(
            input_domain=DictDomain({"left": left_domain, "right": right_domain}),
            input_metric=AddRemoveKeys({"left": "B", "right": "B"}),
            left_key="left",
            right_key="right",
            new_key="joined",
            join_cols=join_cols,
            join_on_nulls=join_on_nulls,
        )

        answer = private_join({"left": left_df, "right": right_df})
        self.assertTrue(answer in private_join.output_domain)
        self.assertEqual(answer["joined"].columns, expected_ordering)

    @parameterized.expand(
        [(1, 1, True), (0, 0, True), (float("inf"), float("inf"), True), (2, 1, False)]
    )
    def test_stability_relation_and_function(
        self, d_in: ExactNumberInput, d_out: ExactNumberInput, expected: bool
    ):
        """Test that PrivateJoinOnKey's stability relation and function are correct"""
        private_join = PrivateJoinOnKey(
            input_domain=DictDomain(
                {"left": self.left_domain, "right": self.right_domain}
            ),
            input_metric=AddRemoveKeys({"left": "B", "right": "B"}),
            left_key="left",
            right_key="right",
            new_key="new",
            join_cols=["B"],
        )
        self.assertEqual(private_join.stability_relation(d_in, d_out), expected)
        self.assertEqual(private_join.stability_function(d_in) <= d_out, expected)

    @parameterized.expand(
        [
            (
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=False, allow_inf=True, allow_nan=False
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=True),
                    }
                ),
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=True),
                        "C": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=False, allow_nan=True
                        ),
                    }
                ),
                ["B"],
                True,
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=True),
                        "A": SparkFloatColumnDescriptor(
                            allow_null=False, allow_inf=True, allow_nan=False
                        ),
                        "C": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=False, allow_nan=True
                        ),
                    }
                ),
            ),
            (
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=True
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=False),
                    }
                ),
                SparkDataFrameDomain(
                    {
                        "A": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=False
                        ),
                        "B": SparkStringColumnDescriptor(allow_null=False),
                    }
                ),
                ["B"],
                True,
                SparkDataFrameDomain(
                    {
                        "B": SparkStringColumnDescriptor(allow_null=False),
                        "A_left": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=True
                        ),
                        "A_right": SparkFloatColumnDescriptor(
                            allow_null=True, allow_inf=True, allow_nan=False
                        ),
                    }
                ),
            ),
        ]
    )
    def test_output_domain_special_values(
        self,
        left_domain: SparkDataFrameDomain,
        right_domain: SparkDataFrameDomain,
        join_cols: List[str],
        join_on_nulls: bool,
        expected_domain: SparkDataFrameDomain,
    ):
        """Tests that special values in output domain."""
        transformation = PrivateJoinOnKey(
            input_domain=DictDomain({"left": left_domain, "right": right_domain}),
            input_metric=AddRemoveKeys({"left": "B", "right": "B"}),
            left_key="left",
            right_key="right",
            new_key="joined",
            join_cols=join_cols,
            join_on_nulls=join_on_nulls,
        )
        self.assertEqual(
            cast(DictDomain, transformation.output_domain).key_to_domain["joined"],
            expected_domain,
        )
